Survival Analysis in Medicine and Genetics

Survival Analysis in Medicine and Genetics
By:"Jialiang Li","Shuangge Ma"
Published on 2013-06-04 by CRC Press

This ebook tells about Using real data sets throughout, Survival Analysis in Medicine and Genetics introduces the latest methods for analyzing high-dimensional survival data. It provides thorough coverage of recent statistical developments in the medical and genetics fields. The text mainly addresses special concerns of the survival model. After covering the fundamentals, it discusses interval censoring, nonparametric and semiparametric hazard regression, multivariate survival data analysis, the sub-distribution method for competing risks data, the cure rate model, and Bayesian inference methods. The authors then focus on time-dependent diagnostic medicine and high-dimensional genetic data analysis. Many of the methods are illustrated with clinical examples. Emphasizing the applications of survival analysis techniques in genetics, this book presents a statistical framework for burgeoning research in this area and offers a set of established approaches for statistical analysis. It reveals a new way of looking at how predictors are associated with censored survival time and extracts novel statistical genetic methods for censored survival time outcome from the vast amount of research results in genomics.

This Book was ranked 32 by Google Books for keyword Survival Analysis.

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Survival Analysis in Medicine and Genetics
145 4.2 Multivariate Survival Data ....................................... 147 4.2.1 ... 148 4.2.2 \u003cbr\u003e\nRegression Analysis of Cluster Data .................... 151 4.2.3 ... 195 The first three \u003cbr\u003e\nchapters cover some common statistical methods for \u003cb\u003esurvival analysis\u003c/b\u003e. Chapter 3 \u003cbr\u003e\nis ...

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Survival Analysis

Survival Analysis
By:"David G. Kleinbaum","Mitchel Klein"
Published on 2013-04-18 by Springer Science & Business Media

This ebook tells about A straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. A \

This Book was ranked 1 by Google Books for keyword Survival Analysis.

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Survival Analysis
A "user-friendly" layout includes numerous illustrations and exercises and the book is written in such a way so as to enable readers learn directly without the assistance of a classroom instructor.

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Applied Survival Analysis

Applied Survival Analysis
By:"David W. Hosmer, Jr.","Stanley Lemeshow"
Published on 1999-01-21 by Wiley-Interscience

This ebook tells about A Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data. The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature. Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include: * Variable selection. * Identification of the scale of continuous covariates. * The role of interactions in the model. * Interpretation of a fitted model. * Assessment of fit and model assumptions. * Regression diagnostics. * Recurrent event models, frailty models, and additive models. * Commercially available statistical software and getting the most out of it. Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields.

This Book was ranked 40 by Google Books for keyword Survival Resources.

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Applied Survival Analysis
This book helps bridge this important gap in the literature. Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research.

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Handbook of Survival Analysis

Handbook of Survival Analysis
By:"John P. Klein","Hans C. van Houwelingen","Joseph G. Ibrahim","Thomas H. Scheike"
Published on 2016-04-19 by CRC Press

This ebook tells about Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

This Book was ranked 28 by Google Books for keyword Survival Analysis.

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Handbook of Survival Analysis
Historically \u003cb\u003esurvival analysis\u003c/b\u003e is one of the oldest areas of statistics dating its origin \u003cbr\u003e\nto classic life table construction begun in the 1600s. Much of the early work in this \u003cbr\u003e\narea involved constructing better life tables and long tedious extensions of ...

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Bayesian Survival Analysis

Bayesian Survival Analysis
By:"Joseph G. Ibrahim","Ming-Hui Chen","Debajyoti Sinha"
Published on 2013-03-09 by Springer Science & Business Media

This ebook tells about Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

This Book was ranked 11 by Google Books for keyword Survival Analysis.

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Bayesian Survival Analysis
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis.

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Survival Analysis

Survival Analysis
By:"John P. Klein","Melvin L. Moeschberger"
Published on 2006-05-17 by Springer Science & Business Media

This ebook tells about Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

This Book was ranked 17 by Google Books for keyword Survival Techniques.

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Survival Analysis
This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

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Dynamic Prediction in Clinical Survival Analysis

Dynamic Prediction in Clinical Survival Analysis
By:"Hans van Houwelingen","Hein Putter"
Published on 2011-11-09 by CRC Press

This ebook tells about There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts: Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated Part III is dedicated to the use of time-dependent information in dynamic prediction Part IV explores dynamic prediction models for survival data using genomic data Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.

This Book was ranked 21 by Google Books for keyword Survival Analysis.

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Dynamic Prediction in Clinical Survival Analysis
In cancer trials the usual endpoint to assess the success of a treatment is “\u003cbr\u003e\n\u003cb\u003esurvival\u003c/b\u003e”. ... In the last thirty years, statisticians have developed many models and \u003cbr\u003e\ntechniques for the \u003cb\u003eanalysis\u003c/b\u003e of data arising in clinical trials or from cancer \u003cbr\u003e\nregistries.

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Frailty Models in Survival Analysis

Frailty Models in Survival Analysis
By:"Andreas Wienke"
Published on 2010-07-26 by CRC Press

This ebook tells about The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

This Book was ranked 15 by Google Books for keyword Survival Analysis.

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Frailty Models in Survival Analysis
Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity.

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Shrinkage Estimation in Nonparametric Bayesian Survival Analysis

Shrinkage Estimation in Nonparametric Bayesian Survival Analysis
By:"Kamta Rai","V. Susarla","John Van Ryzin"
Published on 1979 by

This ebook tells about

This Book was ranked 40 by Google Books for keyword Survival Analysis.

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Shrinkage Estimation in Nonparametric Bayesian Survival Analysis

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An Introduction to Survival Analysis Using Stata, Second Edition

An Introduction to Survival Analysis Using Stata, Second Edition
By:"Mario Cleves"
Published on 2008-05-15 by Stata Press

This ebook tells about An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata's new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.

This Book was ranked 10 by Google Books for keyword Survival Analysis.

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An Introduction to Survival Analysis Using Stata, Second Edition
This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.

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Multivariate Survival Analysis and Competing Risks

Multivariate Survival Analysis and Competing Risks
By:"Martin J. Crowder"
Published on 2012-04-17 by CRC Press

This ebook tells about Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

This Book was ranked 9 by Google Books for keyword Survival Analysis.

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Multivariate Survival Analysis and Competing Risks
Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend.

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Revenue Management and Survival Analysis in the Automobile Industry

Revenue Management and Survival Analysis in the Automobile Industry
By:"André Jerenz"
Published on 2008-08-01 by Springer Science & Business Media

This ebook tells about André Jerenz develops a price-based revenue management framework to support retailers in establishing better and more profitable pricing strategies, including assigning an initial asking price and the adjustment of price over time.

This Book was ranked 33 by Google Books for keyword Survival Analysis.

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Revenue Management and Survival Analysis in the Automobile Industry
According to the requirements defined by Cox and Oakes (1985, p. 1), the used \u003cbr\u003e\ncar sector is an ideal field for \u003cb\u003esurvival analysis\u003c/b\u003e. Consider the case where a used \u003cbr\u003e\nvehicle is displayed by a car retailer for sale. The time origin for the analysis \u003cbr\u003e\nstarts ...

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Survival Analysis Using S

Survival Analysis Using S
By:"Mara Tableman","Jong Sung Kim"
Published on 2003-07-28 by CRC Press

This ebook tells about Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.

This Book was ranked 5 by Google Books for keyword Survival Analysis.

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Survival Analysis Using S
Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology.

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Applied Survival Analysis

Applied Survival Analysis
By:"David W. Hosmer, Jr.","Stanley Lemeshow","Susanne May"
Published on 2011-09-23 by John Wiley & Sons

This ebook tells about THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

This Book was ranked 4 by Google Books for keyword Survival Analysis.

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Applied Survival Analysis
This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory.

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SAS Survival Analysis Techniques for Medical Research

SAS Survival Analysis Techniques for Medical Research
By:"Alan Cantor"
Published on 2003-01-01 by SAS Institute

This ebook tells about In this follow-up to \

This Book was ranked 11 by Google Books for keyword Survival Techniques.

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SAS Survival Analysis Techniques for Medical Research
The new features, along with several useful macros and numerous examples, make this edition a suitable textbook for a course in survival analysis for biostatistics majors and majors in related fields.

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Customer Retention in the Financial Industry: An Application of Survival Analysis

Customer Retention in the Financial Industry: An Application of Survival Analysis
By:
Published on 2008 by ProQuest

This ebook tells about The statistical analyses conducted employing two cohorts showed that the Cox model was stable across cohorts. Using Cox's hazard method, all of the independent variables were found to be significantly related to customer retention, while some of demographic factors (for example, tenure, marital status and gender, and channel usage) were not found to significantly affect customer retention in the logistic regression.

This Book was ranked 35 by Google Books for keyword Survival Analysis.

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Customer Retention in the Financial Industry: An Application of Survival Analysis
Therefore, the static models based on data collected during a specific time period \u003cbr\u003e\ncannot be easily generalized to other periods. By observing customer behavior \u003cbr\u003e\nover time, in a dynamic method, \u003cb\u003esurvival analysis\u003c/b\u003e could address these kinds of ...

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Survival Analysis with Long-Term Survivors

Survival Analysis with Long-Term Survivors
By:"Ross A. Maller","Xian Zhou"
Published on 1996 by John Wiley & Sons

This ebook tells about The aim of this book is to suggest and exemplify a systematic methodology for analysing survival data which contains \

This Book was ranked 24 by Google Books for keyword Survival Analysis.

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Survival Analysis with Long-Term Survivors
The aim of this book is to suggest and exemplify a systematic methodology for analysing survival data which contains "immune", or "cured" individuals, denoted generically as "long-term survivors".

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Mathematical Methods in Survival Analysis, Reliability and Quality of Life

Mathematical Methods in Survival Analysis, Reliability and Quality of Life
By:"Catherine Huber","Nikolaos Limnios","Mounir Mesbah","Mikhail Nikulin"
Published on 2013-03-01 by John Wiley & Sons

This ebook tells about Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

This Book was ranked 34 by Google Books for keyword Survival Analysis.

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Mathematical Methods in Survival Analysis, Reliability and Quality of Life
The European Seminar on Mathematical Methods for \u003cb\u003eSurvival Analysis\u003c/b\u003e, \u003cbr\u003e\nReliability and Quality of Life was created in 1997 by C. HUBER, N. LIMNIOS, M. \u003cbr\u003e\nNIKULIN and M. MESBAH, and, thanks to our respective laboratories and also to \u003cbr\u003e\nthe ...

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Extending SAS Survival Analysis Techniques for Medical Research

Extending SAS Survival Analysis Techniques for Medical Research
By:"Alan B. Cantor"
Published on 1997-01-01 by

This ebook tells about

This Book was ranked 26 by Google Books for keyword Survival Analysis.

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Extending SAS Survival Analysis Techniques for Medical Research

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Survival Analysis Using SAS

Survival Analysis Using SAS
By:"Paul D. Allison"
Published on 2010-03-29 by SAS Institute

This ebook tells about Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program.

This Book was ranked 13 by Google Books for keyword Survival Analysis.

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Survival Analysis Using SAS
This book is part of the SAS Press program.

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